---
language:
- en
license: cc-by-nc-4.0
pipeline_tag: feature-extraction
tags:
- llamafile
library_name: llamafile
base_model: 
- Salesforce/SFR-Embedding-Mistral
- dranger003/SFR-Embedding-Mistral-GGUF
model_creator: Salesforce
quantized_by: dranger003
---
# SFR-Embedding-Mistral - llamafile

This repository contains executable weights (which we call [llamafiles](https://github.com/Mozilla-Ocho/llamafile)) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64.

- Model creator: [Salesforce](https://huggingface.co/Salesforce)
- Original model: [Salesforce/SFR-Embedding-Mistral](https://huggingface.co/Salesforce/SFR-Embedding-Mistral)
- GGUF weights: [dranger003/SFR-Embedding-Mistral-GGUF](https://huggingface.co/dranger003/SFR-Embedding-Mistral-GGUF)
- Built with [llamafile 0.8.4](https://github.com/Mozilla-Ocho/llamafile/releases/tag/0.8.4)
 
## Quickstart

Running the following on a desktop OS will launch a server on `http://localhost:8080` to which you can send HTTP requests to in order to get embeddings:

```
chmod +x ggml-sfr-embedding-mistral-f16.llamafile
./ggml-sfr-embedding-mistral-f16.llamafile --server --nobrowser --embedding
```

Then, you can use your favorite HTTP client to call the server's `/embedding` endpoint:

```
curl \
-X POST \
-H "Content-Type: application/json" \
-d '{"content": "Hello, world!"}' \
http://localhost:8080/embedding
```

For further information, please see the [llamafile README](https://github.com/mozilla-ocho/llamafile/) and the [llamafile server docs](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md).

Having **trouble?** See the ["Gotchas" section](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas) of the README or contact us on [Discord](https://discord.com/channels/1089876418936180786/1182689832057716778).

## About llamafile

llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023.
It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp
binaries that run on the stock installs of six OSes for both ARM64 and
AMD64.

## About Quantization Formats

Your choice of quantization format depends on three things:

1. Will it fit in RAM or VRAM?
2. Is your use case reading (e.g. summarization) or writing (e.g. chatbot)?
3. llamafiles bigger than 4.30 GB are hard to run on Windows (see [gotchas](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas))

Good quants for writing (eval speed) are Q5\_K\_M, and Q4\_0. Text
generation is bounded by memory speed, so smaller quants help, but they
also cause the LLM to hallucinate more.

Good quants for reading (prompt eval speed) are BF16, F16, Q4\_0, and
Q8\_0 (ordered from fastest to slowest). Prompt evaluation is bounded by
computation speed (flops) so simpler quants help.

Note: BF16 is currently only supported on CPU.

See also: https://huggingface.co/docs/hub/en/gguf#quantization-types

---

# Model Card

See [Salesforce/SFR-Embedding-Mistral](https://huggingface.co/Salesforce/SFR-Embedding-Mistral)